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5631464 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 | """HADDOCK3 workflow logic."""
import importlib
import sys
from pathlib import Path
from time import time
from haddock import log
from haddock.clis.cli_analyse import main as cli_analyse
from haddock.clis.cli_traceback import main as cli_traceback
from haddock.core.exceptions import HaddockError, HaddockTermination, StepError
from haddock.core.typing import Any, ModuleParams, Optional
from haddock.gear.clean_steps import clean_output
from haddock.gear.config import get_module_name
from haddock.gear.zerofill import zero_fill
from haddock.libs.libtimer import convert_seconds_to_min_sec, log_time
from haddock.libs.libutil import recursive_dict_update
from haddock.modules import (
modules_category,
non_mandatory_general_parameters_defaults,
)
class WorkflowManager:
"""Read and execute workflows."""
def __init__(
self,
workflow_params: ModuleParams,
start: Optional[int] = 0,
**other_params: Any,
) -> None:
self.start = 0 if start is None else start
self.recipe = Workflow(workflow_params, start=0, **other_params)
# terminate is used to synchronize the `clean` option with the
# `exit` module. If the `exit` module is removed in the future,
# you can also remove and clean the `terminate` part here.
self._terminated = None
def run(self) -> None:
"""High level workflow composer."""
for i, step in enumerate(self.recipe.steps[self.start :], start=self.start):
try:
step.execute()
except HaddockTermination:
self._terminated = i # type: ignore
break
# Forward any params generated at runtime by some module, for example
# auto-generated ligand topology/parameter files to every subsequent
# step that exposes the same key in its config,
# Do this ONLY when the user has not already set a value for that key.
output_params = getattr(step.module, "_output_params", {})
if output_params:
for future_step in self.recipe.steps[i + 1 :]:
for key, value in output_params.items():
if future_step.config.get(key) in (None, ""):
future_step.config[key] = value
def clean(self, terminated: Optional[int] = None) -> None:
"""
Clean steps.
Parameters
----------
terminated : int, None
At which index of the workflow to stop the cleaning. If ``None``,
uses the internal class configuration.
"""
terminated = self._terminated if terminated is None else terminated
for step in self.recipe.steps[:terminated]:
step.clean()
def postprocess(self, self_contained: bool = False) -> None:
"""Postprocess the workflow."""
# is the workflow going to be cleaned?
is_cleaned = self.recipe.steps[0].config["clean"]
# Is the workflow supposed to run offline
offline = self.recipe.steps[0].config["offline"]
# running mode
mode = self.recipe.steps[0].config["mode"]
# ncores
ncores = self.recipe.steps[0].config["ncores"]
capri_steps: list[int] = []
for step in self.recipe.steps:
if step.module_name == "caprieval":
capri_steps.append(step.order) # type: ignore
# call cli_analyse (no need for capri_dicts, it's all precalculated)
cli_analyse(
"./",
capri_steps,
top_clusters=10,
format=None,
scale=None,
inter=False,
is_cleaned=is_cleaned,
offline=offline,
mode=mode,
ncores=ncores,
self_contained=self_contained,
)
# call cli_traceback. If it fails, it's not a big deal
try:
cli_traceback("./", offline=offline)
except Exception as e:
log.warning(f"Error running traceback: {e}")
class Workflow:
"""Represent a set of stages to be executed by HADDOCK."""
def __init__(
self,
modules_parameters: ModuleParams,
start: Optional[int] = 0,
**other_params: Any,
) -> None:
if start is None:
start = 0
# filter out those parameters not belonging to the modules
general_modules = {
k: v
for k, v in other_params.items()
if k in non_mandatory_general_parameters_defaults
}
# Create the list of steps contained in this workflow
self.steps: list[Step] = []
_items = enumerate(modules_parameters.items(), start=start)
for num_stage, (stage_name, params) in _items:
stage_name = get_module_name(stage_name)
log.info(f"Reading instructions step {num_stage}_{stage_name}")
# updates the module's specific parameter with global parameters
# that are applicable to the modules. But keep priority to the local
# level
params_up = recursive_dict_update(general_modules, params)
try:
_ = Step(
stage_name,
order=num_stage,
**params_up,
)
self.steps.append(_)
except StepError as re:
log.error(f"Error found while parsing course {stage_name}")
raise HaddockError from re
class Step:
"""Represents a Step of the Workflow."""
def __init__(
self,
module_name: str,
order: Optional[int] = None,
**config_params: Any,
) -> None:
self.config = config_params
self.module_name = module_name
self.order = order
self.working_path = Path(zero_fill.fill(self.module_name, self.order)) # type: ignore
self.module = None
def execute(self) -> None:
"""Execute simulation step."""
self.working_path.resolve().mkdir(parents=False, exist_ok=False)
# Import the module given by the mode or default
module_name = ".".join(
["haddock", "modules", modules_category[self.module_name], self.module_name]
)
module_lib = importlib.import_module(module_name)
self.module = module_lib.HaddockModule(order=self.order, path=self.working_path)
# Run module
start = time()
try:
self.module.update_params(**self.config) # type: ignore
self.module.save_config(Path(self.working_path, "params.cfg")) # type: ignore
self.module.run() # type: ignore
except KeyboardInterrupt:
log.info("You have halted subprocess execution by hitting Ctrl+c")
log.info("Exiting...")
sys.exit(1)
end = time()
elapsed = convert_seconds_to_min_sec(end - start)
self.module.log(f"took {elapsed}") # type: ignore
def clean(self) -> None:
"""Clean step output."""
if self.module is None and self.config["clean"]:
with log_time("cleaning output files took"):
clean_output(self.working_path, self.config["ncores"])
elif self.module is not None and self.module.params["clean"]:
self.module.clean_output()
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